Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption—both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.

中文翻译：

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无归一化和非参数方法可增强大规模转录组分析并揭示癌症中常见的基因改变模式

转录数据的异质性阻碍了差异表达基因（DEG）的鉴定和对癌症的理解，这主要是因为当前的方法依赖于跨样本归一化和/或分布假设（均对异类值敏感）。在这里，我们开发了一种新的方法，即跨值关联分析（CVAA），该方法克服了该限制，并且比其他方法对异构数据更健壮。将CVAA应用于包含5,540个转录组的更复杂的全癌基因组，发现了许多新的DEG，以及许多以前很少探索的途径/过程。其中一些在体外和体内均被证实在肿瘤发生中至关重要，例如酒精代谢（ADH1B），染色体重塑（NCAPH）和补体系统（Adipsin）。在一起，我们提供了一个更强大的工具来浏览大规模表达数据并获得有关肿瘤发生的新机制的见解。